Use this when your inputs are in string or integer format, and you have an
in-memory vocabulary mapping each value to an integer ID. By default,
out-of-vocabulary values are ignored. Use either (but not both) of
num_oov_buckets and default_value to specify how to include
out-of-vocabulary values.

For input dictionary features, features[key] is either Tensor or
SparseTensor. If Tensor, missing values can be represented by -1 for int
and '' for string, which will be dropped by this feature column.

Example with num_oov_buckets:
In the following example, each input in vocabulary_list is assigned an ID
0-3 corresponding to its index (e.g., input 'B' produces output 2). All other
inputs are hashed and assigned an ID 4-5.

Example with default_value:
In the following example, each input in vocabulary_list is assigned an ID
0-4 corresponding to its index (e.g., input 'B' produces output 3). All other
inputs are assigned default_value 0.

Args:

key: A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
Tensor objects, and feature columns.

vocabulary_list: An ordered iterable defining the vocabulary. Each feature
is mapped to the index of its value (if present) in vocabulary_list.
Must be castable to dtype.

dtype: The type of features. Only string and integer types are supported.
If None, it will be inferred from vocabulary_list.

default_value: The integer ID value to return for out-of-vocabulary feature
values, defaults to -1. This can not be specified with a positive
num_oov_buckets.

num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
buckets. All out-of-vocabulary inputs will be assigned IDs in the range
[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets) based on a
hash of the input value. A positive num_oov_buckets can not be specified
with default_value.